SoroushVahidi/scidocs huggingface dataset:
3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction
(2015)

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.
Advanced PROPHET Routing in Delay Tolerant Network
(2009)

To solve routing jitter problem in PROPHET in delay tolerant network, advanced PROPHET routing is proposed in this paper. Average delivery predictabilities are used in advanced PROPHET to avoid routing jitter. Furthermore, we evaluate it through simulations versus PROPHET routing protocol. The experimental results show there has higher average delivery rates and shorter average delays in advanced PROPHET. Thus, it is fair to say that advanced PROPHET gives better performance than PROPHET.
Error control and concealment for video communication: a review
(1998)

The problem of error control and concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. This paper reviews the techniques that have been developed for error control and concealment in the past ten to fifteen years. These techniques are described in three categories according to the roles that the encoder and decoder play in the underlying approaches. Forward error concealment includes methods that add redundancy at the source end to enhance error resilience of the coded bit streams. Error concealment by postprocessing refers to operations at the decoder to recover the damaged areas based on characteristics of image and video signals. Finally, interactive error concealment covers techniques that are dependent on a dialog between the source and destination. Both current research activities and practice in international standards are covered.
Regenerative braking strategy for electric vehicles
(2009)

Regenerative braking is an effective approach for electric vehicles to extend their driving range. The control strategy of regenerative braking plays an important role in maintaining the vehicle's stability and recovering energy. In this paper, the main properties that have influence on brake energy regeneration are analyzed. Mathematical model of brake energy regenerating electric vehicles is established. By analyzing the charge and discharge characteristics of the battery and motor, a simple regenerative braking strategy is proposed. The strategy takes the braking torque required, the motor available braking torque, and the braking torque limit into account, and it can make the best use of the motor braking torque. Simulation results show higher energy regeneration compared to a parallel strategy when the proposed strategy is adopted.
Periodically Controlled Hybrid Systems Verifying A Controller for An Autonomous Vehicle
(2008)

This paper introduces Periodically Controlled Hybrid Automata (PCHA) for describing a class of hybrid control systems. In a PCHA, control actions occur roughly periodically while internal and input actions, may occur in the interim changing the discrete-state or the setpoint. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariance of PCHAs is presented. This technique is used in verifying safety of the planner-controller subsystem of an autonomous ground vehicle, and in deriving geometric properties of planner generated paths that can be followed safely by the controller under environmental uncertainties.
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
(2017)

Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.
Neural Networks for Open Domain Targeted Sentiment
(2015)

Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combinations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines.
On Throughput Efficiency of Geographic Opportunistic Routing in Multihop Wireless Networks
(2007)

Geographic opportunistic routing (GOR) is a new routing concept in multihop wireless networks. In stead of picking one node to forward a packet to, GOR forwards a packet to a set of candidate nodes and one node is selected dynamically as the actual forwarder based on the instantaneous wireless channel condition and node position and availability at the time of transmission. GOR takes advantages of the spatial diversity and broadcast nature of wireless communications and is an efficient mechanism to combat the unreliable links. The existing GOR schemes typically involve as many as available next-hop neighbors into the local opportunistic forwarding, and give the nodes closer to the destination higher relay priorities. In this paper, we focus on realizing GOR’s potential in maximizing throughput. We start with an insightful analysis of various factors and their impact on the throughput of GOR, and propose a local metric named expected one-hop throughput (EOT) to balance the tradeoff between the benefit (i.e., packet advancement and transmission reliability) and the cost (i.e., medium time delay). We identify an upper bound of EOT and proof its concavity. Based on the EOT, we also propose a local candidate selection and prioritization algorithm. Simulation results validate our analysis and show that the metric EOT leads to both higher one-hop and path throughput than the corresponding pure GOR and geographic routing.
Estimating types in binaries using predictive modeling
(2016)

Reverse engineering is an important tool in mitigating vulnerabilities in binaries. As a lot of software is developed in object-oriented languages, reverse engineering of object-oriented code is of critical importance. One of the major hurdles in reverse engineering binaries compiled from object-oriented code is the use of dynamic dispatch. In the absence of debug information, any dynamic dispatch may seem to jump to many possible targets, posing a significant challenge to a reverse engineer trying to track the program flow. We present a novel technique that allows us to statically determine the likely targets of virtual function calls. Our technique uses object tracelets – statically constructed sequences of operations performed on an object – to capture potential runtime behaviors of the object. Our analysis automatically pre-labels some of the object tracelets by relying on instances where the type of an object is known. The resulting type-labeled tracelets are then used to train a statistical language model (SLM) for each type.We then use the resulting ensemble of SLMs over unlabeled tracelets to generate a ranking of their most likely types, from which we deduce the likely targets of dynamic dispatches.We have implemented our technique and evaluated it over real-world C++ binaries. Our evaluation shows that when there are multiple alternative targets, our approach can drastically reduce the number of targets that have to be considered by a reverse engineer.
Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks
(2018)

Emotions are very important for human intelligence. For example, emotions are closely related to the appraisal of the internal bodily state and external stimuli. This helps us to respond quickly to the environment. Another important perspective in human intelligence is the role of emotions in decision-making. Moreover, the social aspect of emotions is also very important. Therefore, if the mechanism of emotions were elucidated, we could advance toward the essential understanding of our natural intelligence. In this study, a model of emotions is proposed to elucidate the mechanism of emotions through the computational model. Furthermore, from the viewpoint of partner robots, the model of emotions may help us to build robots that can have empathy for humans. To understand and sympathize with people’s feelings, the robots need to have their own emotions. This may allow robots to be accepted in human society. The proposed model is implemented using deep neural networks consisting of three modules, which interact with each other. Simulation results reveal that the proposed model exhibits reasonable behavior as the basic mechanism of emotion.
Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions.
(2005)

OBJECTIVE
To present nationally representative data on lifetime prevalence and comorbidity of pathological gambling with other psychiatric disorders and to evaluate sex differences in the strength of the comorbid associations.


METHOD
Data were derived from a large national sample of the United States. Some 43,093 household and group quarters residents age 18 years and older participated in the 2001-2002 survey. Prevalence and associations of lifetime pathological gambling and other lifetime psychiatric disorders are presented. The diagnostic interview was the National Institute on Alcohol Abuse and Alcoholism Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version. Fifteen symptom items operationalized the 10 pathological gambling criteria.


RESULTS
The lifetime prevalence rate of pathological gambling was 0.42%. Almost three quarters (73.2%) of pathological gamblers had an alcohol use disorder, 38.1% had a drug use disorder, 60.4% had nicotine dependence, 49.6% had a mood disorder, 41.3% had an anxiety disorder, and 60.8% had a personality disorder. A large majority of the associations between pathological gambling and substance use, mood, anxiety, and personality disorders were overwhelmingly positive and significant (p < .05), even after controlling for sociodemographic and socioeconomic characteristics. Male sex, black race, divorced/separated/widowed marital status, middle age, and living in the West and Midwest were associated with increased risk for pathological gambling. Further, associations between alcohol dependence, any drug use disorder, drug abuse, nicotine dependence, major depressive episode, and generalized anxiety disorder and pathological gambling were stronger among women than men (p > .05).


CONCLUSION
Pathological gambling is highly comorbid with substance use, mood, anxiety, and personality disorders, suggesting that treatment for one condition should involve assessment and possible concomitant treatment for comorbid conditions.
Content-based image retrieval by integrating color and texture features
(2012)

Content-based image retrieval (CBIR) has been an active research topic in the last decade. Feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a content-based image retrieval method based on an efficient integration of color and texture features. As its color features, pseudo-Zernike chromaticity distribution moments in opponent chromaticity space are used. As its texture features, rotation-invariant and scale-invariant image descriptor in steerable pyramid domain are adopted, which offers an efficient and flexible approximation of early processing in the human visual system. The integration of color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for different test DBs.
Rfam 12.0: updates to the RNA families database
(2015)

The Rfam database (available at http://rfam.xfam.org) is a collection of non-coding RNA families represented by manually curated sequence alignments, consensus secondary structures and annotation gathered from corresponding Wikipedia, taxonomy and ontology resources. In this article, we detail updates and improvements to the Rfam data and website for the Rfam 12.0 release. We describe the upgrade of our search pipeline to use Infernal 1.1 and demonstrate its improved homology detection ability by comparison with the previous version. The new pipeline is easier for users to apply to their own data sets, and we illustrate its ability to annotate RNAs in genomic and metagenomic data sets of various sizes. Rfam has been expanded to include 260 new families, including the well-studied large subunit ribosomal RNA family, and for the first time includes information on short sequence- and structure-based RNA motifs present within families.
Marginalized Denoising Autoencoders for Domain Adaptation
(2012)

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters — in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.
Multi-Task Learning for Mental Health using Social Media Text
(2017)

We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multitask learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.
Co-Training for Domain Adaptation
(2011)

Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical cases, the source and target distributions can differ substantially, and in some cases crucial target features may not have support in the source domain. In this paper we introduce an algorithm that bridges the gap between source and target domains by slowly adding to the training set both the target features and instances in which the current algorithm is the most confident. Our algorithm is a variant of co-training [7], and we name it CODA (Co-training for domain adaptation). Unlike the original co-training work, we do not assume a particular feature split. Instead, for each iteration of cotraining, we formulate a single optimization problem which simultaneously learns a target predictor, a split of the feature space into views, and a subset of source and target features to include in the predictor. CODA significantly out-performs the state-of-the-art on the 12-domain benchmark data set of Blitzer et al. [4]. Indeed, over a wide range (65 of 84 comparisons) of target supervision CODA achieves the best performance.
A Planar Magic-T Using Substrate Integrated Circuits Concept
(2008)

In this letter, a slotline to substrate integrated waveguide transition is proposed for the development of substrate integrated circuits. The insertion loss of the back-to-back transition is less than 1 dB from 8.7 to 9.0 GHz. With this transition, a planar magic-T is studied and designed. Measured results indicate a very good performance of the fabricated magic-T is observed within the experimental frequency range of 8.4-9.4 GHz. The amplitude and phase imbalances are less than 0.2 dB and 1.5deg, respectively.
Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration
(2010)

Vessel enhancement and segmentation aim at (binary) per-pixel segmentation considering certain local features as probabilistic vessel indicators. We propose a new methodology to combine any local probability map with local directional vessel information. The resulting global vessel segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information. The streamlines are computed by numerical integration of the directional vector field that is obtained from the eigenanalysis of the local Hessian indicating the local vessel direction. The streamline representation allows for sophisticated post-processing techniques using the additional information to refine the segmentation result with respect to the requirements of the particular application such as image registration. We propose different post-processing techniques for hierarchical segmentation, centerline extraction, and catheter removal to be used for X-ray angiograms. We further demonstrate how the global approach is able to significantly improve the segmentation compared to conventional local Hessian-based approaches.
Control of VSC connected to the grid through LCL-filter to achieve balanced currents
(2005)

Grid-connected voltage source converters (VSCs) are the heart of many applications with power quality concerns due to their reactive power controllability. However, the major drawback is their sensitivity to grid disturbances. Moreover, when VSCs are used in DG applications, voltage unbalance may be intolerant. The current protection may trip due to current unbalance or due to overcurrent. In this paper, a vector current controller for VSC connected to the grid through LCL-filter is developed with the main focus on producing symmetrical and balanced currents in case of unbalanced voltage dips. Implementing this controller helps the VSC system not to trip during voltage dips.
Marketing malpractice: the cause and the cure.
(2005)

Ted Levitt used to tell his Harvard Business School students, "People don't want a quarter-inch drill--they want a quarter-inch hole." But 35 years later, marketers are still thinking in terms of products and ever-finer demographic segments. The structure of a market, as seen from customers' point of view, is very simple. When people need to get a job done, they hire a product or service to do it for them. The marketer's task is to understand what jobs periodically arise in customers' lives for which they might hire products the company could make. One job, the "I-need-to-send-this-from-here-to-there-with-perfect-certainty-as-fast-as-possible"job, has existed practically forever. Federal Express designed a service to do precisely that--and do it wonderfully again and again. The FedEx brand began popping into people's minds whenever they needed to get that job done. Most of today's great brands--Crest, Starbucks, Kleenex, eBay, and Kodak, to name a few-started out as just this kind of purpose brand. When a purpose brand is extended to products that target different jobs, it becomes an endorser brand. But, over time, the power of an endorser brand will surely erode unless the company creates a new purpose brand for each new job, even as it leverages the endorser brand as an overall marker of quality. Different jobs demand different purpose brands. New growth markets are created when an innovating company designs a product and then positions its brand on a job for which no optimal product yet exists. In fact, companies that historically have segmented and measured markets by product categories generally find that when they instead segment by job, their market is much larger (and their current share much smaller) than they had thought. This is great news for smart companies hungry for growth.
Cubrick : A Scalable Distributed MOLAP Database for Fast Analytics
(2015)

This paper describes the architecture and design of Cubrick, a distributed multidimensional in-memory database that enables real-time data analysis of large dynamic datasets. Cubrick has a strictly multidimensional data model composed of dimensions, dimensional hierarchies and metrics, supporting sub-second MOLAP operations such as slice and dice, roll-up and drill-down over terabytes of data. All data stored in Cubrick is chunked in every dimension and stored within containers called bricks in an unordered and sparse fashion, providing high data ingestion ratios and indexed access through every dimension. In this paper, we describe details about Cubrick’s internal data structures, distributed model, query execution engine and a few details about the current implementation. Finally, we present some experimental results found in a first Cubrick deployment inside Facebook.
A comparative study of HTM and other neural network models for online sequence learning with streaming data
(2016)

Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms. HTM and long-short term memory (LSTM) give the best prediction accuracy. HTM additionally demonstrates many other features that are desirable for real-world sequence learning, such as fast adaptation to changes in the data stream, robustness to sensor noise and fault tolerance. These features make HTM an ideal candidate for online sequence learning problems.